Neural Nets and Surroundings pp 93-100

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 19) | Cite as

PM10 Forecasting Using Kernel Adaptive Filtering: An Italian Case Study

  • Simone Scardapane
  • Danilo Comminiello
  • Michele Scarpiniti
  • Raffaele Parisi
  • Aurelio Uncini

Abstract

Short term prediction of air pollution is gaining increasing attention in the research community, due to its social and economical impact. In this paper we study the application of a Kernel Adaptive Filtering (KAF) algorithm to the problem of predicting PM10 data in the Italian province of Ancona, and we show how this predictor is able to achieve a significant low error with the inclusion of chemical data correlated with the PM10 such as NO2.

Keywords

Air pollution Nonlinear adaptive filtering Kernel Adaptive Filters Least Mean Square 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Simone Scardapane
    • 1
  • Danilo Comminiello
    • 1
  • Michele Scarpiniti
    • 1
  • Raffaele Parisi
    • 1
  • Aurelio Uncini
    • 1
  1. 1.Department of Information Engineering, Electronics and Telecommunications (DIET)“Sapienza” University of RomeRomeItaly

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